Making Waves at CVPR: Inception Startups Exhibit GPU-Powered Work in Long Beach
Computer vision technology that can identify items in a shopping bag. Deep learning tools that inspect train tracks for defects. An AI model that automatically labels street-view imagery.
These are just a few of the AI breakthroughs being showcased this week by the dozens of NVIDIA Inception startups at the annual Computer Vision and Pattern Recognition conference, one of the world’s top AI research events.
The NVIDIA Inception virtual accelerator program supports startups harnessing GPUs for AI and data science applications. Since its launch in 2016, the program has expanded over tenfold in size, to over 4,000 companies. More than 50 of them can be found in the CVPR expo hall — exhibiting GPU-powered work spanning retail, robotics, healthcare and beyond.
Malong Technologies: Giving Retailers an Edge with AI
From self-serve weighing stations that automatically identify fresh produce items in a plastic shopping bag, to smart vending machines that can recognize when a shopper takes a beverage out of a cooler — product recognition AI developed by Malong Technologies is enabling frictionless shopping experiences.
Malong’s computer vision solutions are transforming traditional retail equipment into smarter devices, enabling machines to see the products within them to improve operational efficiency, security and the customer experience.
Using the NVIDIA Metropolis platform for smart cities, the company is building product recognition AI models that enable highly accurate, real-time decisions at the edge. Malong develops powerful, scalable intelligent video analytics tools that can accurately recognize hundreds of thousands of retail products in real time. The company researches weakly-supervised learning to significantly reduce the effort to retrain their models as product packaging and store environments change.
Malong was able to speed its inferencing by more than 40x compared to CPU when using DeepStream and TensorRT software libraries on the NVIDIA T4 GPU. The company uses NVIDIA V100 GPUs in the cloud for training, and the Jetson TX2 supercomputer on a module to bring true AI computing at the edge.
At CVPR, the company is at booth 1316 on the show floor and is presenting research that achieves a new gold standard for image retrieval, outperforming prior methods by a significant margin. Malong is also co-hosting the Fine-Grained Visual Categorization Workshop and organized the first ever retail product recognition challenge at CVPR.
ABEJA: Keeping Singapore’s Metros on Track
Manually inspecting railway tracks is a dangerous task, often done by workers at night when trains aren’t running. But with high-speed cameras, transportation companies can instead capture images of the tracks and use AI to automatically detect defects for railway maintenance.
ABEJA, based in Japan, is developing deep learning models that detects anomalies on tracks with more than 90 percent accuracy, a significant improvement over other automated inspection methods. The startup works with SMRT, Singapore’s leading public transport operator, to examine rail defects.
Founded in 2012, ABEJA builds deep learning tools for multiple industries, including retail, manufacturing and infrastructure. Other use cases include an AI to measure efficiency in car factories and a natural language processing model to provide insights for call centers.
The company uses NVIDIA GPUs on premises and in the cloud for training its AI models. For inference, ABEJA has used GPUs for real-time data processing and high-performance image segmentation projects. It has also deployed projects using NVIDIA Jetson TX2 for AI inference at the edge.
The startup is showing a demo of the ABEJA annotation model in its CVPR booth.
Mapillary: AI in the Streets
Sweden-based Mapillary uses computer vision to automate mapping. Its AI models break down and classify street-level images, segmenting and labeling elements like roads, lane markings, street lights and sidewalks. The company has to date processed hundreds of millions of images submitted by individual contributors, nonprofit organizations, companies and governments worldwide.
These labeled datasets can be used for various purposes, including to create useful maps for local governments, train self-driving cars, or build tools for people with disabilities.
Mapillary is presenting four papers at CVPR this year, including one titled Seamless Scene Segmentation. The model described in the research — a new approach that joins two AI models into one, setting a new state-of-the-art for performance — was trained on eight NVIDIA V100 GPUs.
The segmentation models featured in Mapillary’s CVPR booth were also trained using V100 GPUs. By adopting the NVIDIA TensorRT inference software stack in 2017, Mapillary was able to speed up its segmentation algorithms by up to 27x when running on the Amazon Web Services cloud.
Companies interested in the NVIDIA Inception virtual accelerator can visit the program website and apply to join. Inception members are eligible for a 20 percent discount on up to six NVIDIA TITAN RTX GPUs until Oct. 26.
Startups based in the following countries can request a discount code by emailing firstname.lastname@example.org: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Spain, Sweden, United Kingdom, United States.
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